Agent skill · writing content · affaan-m
strategic-compact
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction. Use when a session is approaching a context limit and a task phase is a natural place to compact.
Why this skill is useful
Adds an executable script for context management that optimizes AI performance during long sessions and multi-phase tasks.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 238,342 stars on the source repository.
What this skill does
Strategic Compact Skill Suggests manual /compact at strategic points in your workflow rather than relying on arbitrary auto-compaction. When to Activate Running long sessions that approach context limits (200K+ tokens) Working on multi-phase tasks (research → plan → implement → test) Switching between unrelated tasks within the same session After completing a major milestone and starting new work When responses slow down or become less coherent (context pressure) Why Strategic Compaction? Auto-compaction triggers at arbitrary points: Often mid-task, losing important context No awareness of logical task boundaries Can interrupt complex multi-step operations Strategic compaction at logical boundaries: After exploration, before execution — Compact research context, keep implementation plan After completing a milestone — Fresh start for next phase Before major context shifts — Clear exploration context before different task How It Works The suggest-compact.js script runs on PreToolUse (Edit/Write) and combines two signals: 1. Context size (primary) — Reads the latest usage record from the session transcript (transcriptpath in the hook payload) and sums inputtokens + cachereadinputtokens + cachecreationinputtokens (the true context size of the turn). Suggests /compact at a window-scaled threshold — 160k tokens on a 200k window, 250k on a 1M window (detected from a [1m] model marker, or inferred when observed tokens already exceed 200k) — and re-reminds after every additional 60k tokens of context growth. A valid native compaction budget can lower the default reminder threshold to 80% of that budget; model-capacity percentages still use the resolved model window 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills affaan-m/strategic-compact--94a119